Automatic detection and recognition of traffic signs in stereo images based on features and probabilistic neural networks

Considering the problem of automatic traffic sign detection and recognition in stereo images captured under motion conditions, a new algorithm for traffic sign detection and recognition based on features and probabilistic neural networks (PNN) is proposed in this paper. Firstly, global statistical color features of left image are computed based on statistics theory. Then for red, yellow and blue traffic signs, left image is segmented to three binary images by self-adaptive color segmentation method. Secondly, gray-value projection and shape analysis are used to confirm traffic sign regions in left image. Then stereo image matching is used to locate the homonymy traffic signs in right image. Thirdly, self-adaptive image segmentation is used to extract binary inner core shapes of detected traffic signs. One-dimensional feature vectors of inner core shapes are computed by central projection transformation. Fourthly, these vectors are input to the trained probabilistic neural networks for traffic sign recognition. Lastly, recognition results in left image are compared with recognition results in right image. If results in stereo images are identical, these results are confirmed as final recognition results. The new algorithm is applied to 220 real images of natural scenes taken by the vehicle-borne mobile photogrammetry system in Nanjing at different time. Experimental results show a detection and recognition rate of over 92%. So the algorithm is not only simple, but also reliable and high-speed on real traffic sign detection and recognition. Furthermore, it can obtain geometrical information of traffic signs at the same time of recognizing their types.

[1]  Francisco López-Ferreras,et al.  Road-Sign Detection and Recognition Based on Support Vector Machines , 2007, IEEE Transactions on Intelligent Transportation Systems.

[2]  Xiaohong W. Gao,et al.  Recognition of traffic signs based on their colour and shape features extracted using human vision models , 2006, J. Vis. Commun. Image Represent..

[3]  Zhu Shuang-dong,et al.  Intelligent Approach for Triangle Traffic Sign Detection , 2006 .

[4]  Chung-Lin Huang,et al.  Road sign detection and recognition using matching pursuit method , 2001, Image Vis. Comput..

[5]  Han Liu,et al.  Real-time recognition of road traffic sign in motion image based on genetic algorithm , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[6]  Tae Young Choi,et al.  Vision-Based Roadway Sign Recognition , 2000 .

[7]  Bahram Javidi,et al.  Nonlinear distortion-tolerant filters for detection of road signs in background noise , 2002, IEEE Trans. Veh. Technol..

[8]  Paulo Lobato Correia,et al.  Automatic Detection and Classification of Traffic Signs , 2007, Eighth International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS '07).

[9]  Arturo de la Escalera,et al.  Traffic sign recognition and analysis for intelligent vehicles , 2003, Image Vis. Comput..

[10]  M. Benallal,et al.  Real-time color segmentation of road signs , 2003, CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436).

[11]  S. Lafuente-Arroyo,et al.  Traffic sign shape classification evaluation I: SVM using distance to borders , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[12]  Yuan Yan Tang,et al.  Feature extraction using wavelet and fractal , 2001, Pattern Recognit. Lett..

[13]  Donald F. Specht,et al.  Probabilistic neural networks and the polynomial Adaline as complementary techniques for classification , 1990, IEEE Trans. Neural Networks.

[14]  H. Fleyeh,et al.  Invariant Road Sign Recognition with Fuzzy ARTMAP and Zernike Moments , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[15]  P. Gil-Jim.  Traffic sign shape classification based on Support Vector Machines and the FFT of the signature of blobs , 2007 .

[16]  Robert P. W. Duin,et al.  Building Road-Sign Classifiers Using a Trainable Similarity Measure , 2006, IEEE Transactions on Intelligent Transportation Systems.

[17]  Shuangdong Zhu.  Two hierarchy classifier for recognition of traffic signs based on neural network , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[18]  A. Zelinsky,et al.  Real-time radial symmetry for speed sign detection , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[19]  Yasutami Chigusa,et al.  Angle-dependent edge detection for traffic signs recognition , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).